International Journal of Artificial Intelligence, Data Science, and Machine Learning
type: channel
language: en
publication name: International Journal of Artificial Intelligence, Data Science, and Machine Learning
issn: 3050-9254
items: Predictive Capacity Management in Teradata: AI-Driven Forecasting and Performance Optimization for Enterprise Data Warehouses
items: Developing Healthcare Knowledge Graphs through Graph Neural Networks
items: Secure Healthcare Data Sharing Ecosystems over Blockchain
items: A Generative AI Framework for Data Pipeline Optimization and Analytical Performance Enhancement
items: Generative AI–Enabled Intelligent Query Optimization for Large-Scale Data Analytics Platforms
items: Using Salesforce CRM and Deep Learning (CNN) Techniques to Improve Patient Journey Mapping and Engagement in Small and Medium Healthcare Organizations
items: Quantum Algorithms for Optimization and Machine Learning
items: Dynamic Workload Placement across Multi-Clouds: AI-Driven Cost Optimization without Downtime
items: Securing Broadband Subscriptions on SaaS with OAuth2.0 in REST API
items: Use of Federated Learning in Iiot (Industrial Internet of Things) Security
items: The Role of Artificial Intelligence in Predicting Credit Risk
items: Temporal Graph Neural Networks for Real-Time Fraud Detection in Cross-Border Transactions
items: The Effectiveness of Social Media in Customer Risk Awareness
items: AI-Enhanced Event Tracking: A Collaborative Full-Stack Model for Tag Intelligence and Real-Time Data Validation
items: Governance-of-Things (GoT): A Next-Generation Framework for Ethical, Intelligent, and Autonomous Web Data Acquisition
items: AI-Enabled Policy-Driven Web Governance: A Full-Stack Java Framework for Privacy-Preserving Digital Ecosystems
items: What’s the True ROI of Copay Programs? A Transparent Framework for Evaluation
items: Generative AI for Network Attack Simulation
items: Federated Learning Infrastructure for Privacy-Preserving Personalized Shopping in E-Commerce
items: Exponential Pine Cone Optimization Enabled Quantum-Inspired Convolutional Neural Networks for Secure Network Slicing in 5G Network
items: Architecting, Grounding and Governing – Telecom Supply Chain GenAI Agents with SAP AI Foundation
items: Privacy-Preserving Federated Learning Frameworks for Telematics Data in Auto Insurance Analytics
items: Conversational AI in Salesforce: A Study of Einstein Bots and Natural Language Understanding
items: Hybrid AI-Based Threat Prediction and Mitigation Framework for Secure Cloud Storage: A Rigorous Critical Review of Methodological Challenges and Future Research Directions
items: AI-Enhanced ETL Framework for Improving Data Quality in Clinical Decision Support Systems
items: Big Data Pipeline Optimisation for Electronic Health Records (EHR)
items: Data Quality Assessment and Cleaning Framework for Healthcare Databases Using Python
items: Encryption Strategies for Secure Big Data Storage: A Study of AWS S3 and Redshift Clusters
items: When AI Acts: Opportunities and Risks of Agentic Systems
items: Emergence of AI Trust Layers & Governance

Predictive Capacity Management in Teradata: AI-Driven Forecasting and Performance Optimization for Enterprise Data Warehouses
type: item
description: As more and more businesses rely on their Teradata for in-depth analytics, keeping an eye on the capacity of their data warehouses has become a big worry. Traditional static methods don't always function well with the changing nature of data growth, workload changes & changing their business needs. This article talks about a way to use AI to manage predictive capacity in Teradata. It focuses on their anticipatory forecasting & how they use their resources strategically. The suggested method uses ML models to look at how much storage, workload & query their performance have changed over time to make accurate guesses about how much capacity they will need in the future. It lets companies detect infrastructure problems before they happen, change how much storage & computing power they need on the go & keep the system running at its best without providing too much. The research uses anomaly detection to find many sudden spikes in workload & these idle resources. This ensures the system works well & expenses the least amount of money. Experimental results from enterprise-scale datasets demonstrate these significant enhancements in workload predictability, query response times & system throughput, yielding up to a 25% increase in their resource utilization and a 30% reduction in operating expenses compared to conventional threshold-based methodologies. The design makes sure that IT infrastructure is in line with actual business needs while also making sure that it can grow & be more reliable. This AI-powered strategy for managing these predictive capacity converts Teradata settings into these self-optimizing ecosystems. These ecosystems maintain their performance & efficiency high even as data expands and business analytics demands change
subject: Articles
  topic: section
subject: Teradata
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Capacity Management
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Forecasting
  topic: keywords
subject: Data Warehouse Optimization
  topic: keywords
subject: AI Operations (AIOps)
  topic: keywords
creator: Guruprasad Nookala
rights: Copyright (c) 2025
date: 2025-08-27
publication date: 2025-08-27
starting page: 115
ending page: 124
doi: 10.63282/3050-9262.IJAIDSML-V6I3P117

Developing Healthcare Knowledge Graphs through Graph Neural Networks
type: item
description: Healthcare Knowledge Graphs (KGs) have evolved into many powerful tools for organizing & connecting huge amounts of medical information into meaningful connections that may help with these clinical insights & decision-making. Nonetheless, the creation of effective knowledge graphs in their healthcare is challenging because of the diversity & complexity of medical data sources, including electronic health records, biological literature & genetic databases, each with unique formats & terminologies. This heterogeneity often leads to these inconsistencies, hindering the achievement of semantic interoperability & accurate data integration. This research explores the use of Graph Neural Networks (GNNs) for improving the intelligence as well as adaptability of medical data graphs in addressing those challenges. The proposed strategy leverages the representational capabilities of GNNs to increase learning from graph-structured information, therefore clarifying more complex relationships across many patients, diseases, treatments & biological entities. By finding subconscious patterns, identifying missing interactions & improving the knowledge graph's whole reasoning ability stronger, the method makes knowledge inference stronger. Experiments indicate that the use of GNNs substantially improves entity connections, connection estimations & diagnostic proposals in comparison to these traditional rule-based or statistical approaches. The results show how integrating graph-based instructional methods with their way of displaying health-related data might lead to evolving, interpretable & adaptable platforms that improve clinical choice-making, individualized treatment planning & medical research. This study demonstrates how enhanced GNN medical understanding graphs might boost the interconnection, data-driven nature & cognitive abilities of healthcare systems.
subject: Articles
  topic: section
subject: Healthcare Knowledge Graphs
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Clinical Data Mining
  topic: keywords
subject: Medical Ontologies
  topic: keywords
subject: Data Integration
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Health Informatics
  topic: keywords
subject: Knowledge Representation
  topic: keywords
creator: Sarbaree Mishra
rights: Copyright (c) 2025
date: 2025-11-25
publication date: 2025-11-25
starting page: 122
ending page: 131
doi: 10.63282/3050-9262.IJAIDSML-V6I4P117

Secure Healthcare Data Sharing Ecosystems over Blockchain
type: item
description: In the present day's digital world, when privacy, accuracy & quick access to patient information directly affect medical outcomes, the safe & effective sharing of healthcare information has become a major issue. Even while these centralized data management systems are very common, they frequently run into many problems including data silos, poor interoperability & being vulnerable to these breaches or unauthorized access. These restrictions make it harder for healthcare providers, patients & researchers to work together, and they also hurt the confidence and integrity of their information. Blockchain technology offers a decentralized and unchangeable system that ensures their transparency, security, and smooth data flow amongst all parties involved in order to solve these problems. This research introduces a blockchain-based structure for the exchange of healthcare data, using distributed ledger technology to provide secure access control, immutable audit trails & interoperability across electronic health record (EHR) systems via the use of smart contracts. The framework uses a permissioned blockchain architecture to balance privacy as well as scalability. This lets healthcare companies keep ownership of their information while allowing controlled data sharing. A case study shows that the proposed technique greatly enhances data security, makes it easier for administrators to conduct their jobs & makes it easier to handle patient consent compared to other methods. Also, the blockchain-based technology makes it easier to track their information and build trust amongst stakeholders, which leads to a more open & cooperative healthcare environment. The results show that implementing blockchain to healthcare systems decreases data-associated risks and supports the development of patient-oriented, interoperable, and ethically sound health data infrastructures that could influence how healthcare is administered throughout the entire globe.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Healthcare Data Sharing
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Data Security
  topic: keywords
subject: Decentralized Systems
  topic: keywords
creator: Sarbaree Mishra
creator: Sai Veera Rupesh Shiramalla
rights: Copyright (c) 2025
date: 2025-11-28
publication date: 2025-11-28
starting page: 132
ending page: 141
doi: 10.63282/3050-9262.IJAIDSML-V6I4P118

A Generative AI Framework for Data Pipeline Optimization and Analytical Performance Enhancement
type: item
description: The surprising increase in data-intensive workloads in enterprise and cloud environments has increased the call to find smart and agile methods of optimizing data pipelines. The dynamic nature of the contemporary modern analytical ecosystems makes traditional rule-based and fixed optimization strategies inadequate, leading to bottlenecks, latency as well as poor utilization of resources. The given paper suggests a Generative AI-based framework that analyzes, designs, and optimizes data pipelines autonomously to improve the overall analytical performance. The architecture uses large language models (LLM) and reinforcement learning to identify optimal pipeline designs, dynamically adapt resource deployment and optimize flow real-time tune ethics. The architecture also incorporates an automatic response loop of self-optimization constantly learning in response to workload trends, metadata and past performance indicators. Single-run benchmark performance of analytics workloads on experimental platforms show a substantial decrease of pipeline latency 42.7%, throughput 58.4% and compute cost 31.2% in comparison with automation benchmark methods. In addition, its machine engine record attained 92.3% percent accuracy in pipeline recommendation that accentuates its flexibility in different data processing settings. The suggested design provides the basis of self-engineered data architecture to support scalable cost-effective and self-optimizing analytic engines and grocery data valuable operations in the next generation of data-driven businesses
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Data Pipeline
  topic: keywords
subject: Optimization
  topic: keywords
subject: Analytical Performance
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Automation
  topic: keywords
subject: Model-driven Systems
  topic: keywords
subject: Cloud Analytics
  topic: keywords
creator: Dinesh Babu Govindarajulunaidu Sambath Narayanan
rights: Copyright (c) 2025
date: 2025-11-25
publication date: 2025-11-25
starting page: 110
ending page: 121
doi: 10.63282/3050-9262.IJAIDSML-V6I4P116

Generative AI–Enabled Intelligent Query Optimization for Large-Scale Data Analytics Platforms
type: item
description: The immense scale of data volume in the distributed cloud infrastructures has magnified the computational intensity of query processing in extensive analytics system like Data Lake, distributed SQL systems and real-time data warehouses. The rule-based and cost-based algorithm-based traditional query optimization methods are becoming inadequate with dynamics, heterogeneous sources of data, and unpredictable execution conditions. The study presents an Intelligent Query Optimization Framework that relies on an AI to automatically rewrite the queries, optimize the plans, and execute them dynamically using the Generative Artificial Intelligence (Gen-AI). The proposed system term employs reinforcement learning (RL), natural language processing (NLP), and deep neural architecture to produce optimized query schemes, approximate the best implementation plans, decrease latency and enhance the throughput in gigacursing assemblies. Workloads, execution history of queries and run-time performance metrics on the distributed systems like Apache Spark, Presto, and Snowflake are all used to train the system. The research contributions consist of: (i) a synthesis mechanism to generate query plans through the use of transformer-based models, (ii) modeling to predict the costs of the workload, (iii) a multi-objective optimization scheme that minimizes the execution time, resource consumption, and cost of data transfer, and (iv) a hybrid architecture in which a batch and a streaming analytics is executed. Extensive experimental findings using the TPC-H benchmark show how it improves performance by 41%-percentage change in query latency, 32-percentage changes in throughput, and 27-percentage changes in memory usage as compared to state-of-the-art optimizers. The presented framework can be scaled, flexible and proficient in the changing data environment, which is a remarkable breakthrough in intelligent data analytics
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Query Optimization
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cost-Based Optimization
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Distributed Databases
  topic: keywords
subject: Data Lakes
  topic: keywords
creator: Dinesh Babu Govindarajulunaidu Sambath Narayanan
rights: Copyright (c) 2025
date: 2025-05-20
publication date: 2025-05-20
starting page: 153
ending page: 160
doi: 10.63282/3050-9262.IJAIDSML-V6I2P117

Using Salesforce CRM and Deep Learning (CNN) Techniques to Improve Patient Journey Mapping and Engagement in Small and Medium Healthcare Organizations
type: item
description: Healthcare firms are becoming more and more reliant on predictive analytics and adaptive technology solutions to promote this engagement with the patient to make operations more efficient now and in the future. This paper investigates the process of illustrating merging of Convolutional Neural Network (CNN) as a class of deep neural networks in Salesforce CRM today by fine tuning the implementation of CNN to work on large datasets to deliver personalization and lifecycle governance of the Patient journey with the basic objective to compliment small and medium healthcare organizations across India. Using a mixed-methods design, quantitative data from 450 healthcare facilities are combined with CNN-based predictive modeling to examine points of entry into care, admission forecasts, and predictions of lifecycle transitions. The hypothesis states that the integration of CNN with the CRM systems can lead to significant improvements in patient satisfaction scores, reduce administrative costs and enhanced care coordination as compared to traditional CRM implementations. The result is a 34% increase in patient engagement metrics, 28% decrease in no-shows for appointments, and 41% increase in treatment adherence rates. Statistical analysis indicates significant association of predictive accuracy and optimization of patient outcomes. The CNN model could indicate transitions within patients lifecycle with 92% accuracy, enabling preventive actions. Then it discuss the learning-enabled CRM systems as transformative in resource-limited healthcare environments, focusing on scalability and ethics. Low-cost yet robust adaptive technologies are needed to ensure sustainable healthcare delivery during the pandemic and beyond, as is the need for policy frameworks that support digital transformation to meet healthcare demand in emerging healthcare markets
subject: Articles
  topic: section
subject: Convolutional Neural Networks
  topic: keywords
subject: Salesforce CRM
  topic: keywords
subject: Patient Journey Optimization
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
subject: Lifecycle Governance
  topic: keywords
creator: Achuta Krishna Kishore Varma Alluri
rights: Copyright (c) 2025
date: 2025-11-22
publication date: 2025-11-22
starting page: 101
ending page: 109
doi: 10.63282/3050-9262.IJAIDSML-V6I4P115

Quantum Algorithms for Optimization and Machine Learning
type: item
description: Quantum computing offers a fundamentally new paradigm for solving complex optimisation and learning tasks by exploiting superposition, entanglement, and other quantum phenomena. Over the past decade, research has increasingly focused on quantum-enhanced approaches to optimisation and machine learning (ML), showing the potential to outperform classical methods in specific settings (Zaman, 2023; Peral-García, 2024). This paper presents a comprehensive review of quantum algorithms for optimisation (e.g., the Quantum Approximate Optimization Algorithm and quantum annealing) and quantum machine learning frameworks (such as quantum support vector machines and quantum neural networks), analysing their theoretical underpinnings, implementation status, and applicability to real-world tasks. Key challenges including hardware noise, limited qubit counts, and algorithmic scalability are examined (Chen, 2024). We also explore hybrid quantum-classical architectures as a near-term route to quantum advantage and propose future research directions aimed at bridging the current gap between quantum algorithm theory and large-scale deployment. Through this synthesis, we aim to provide both academic and practitioner audiences with a clear roadmap for leveraging quantum algorithms in optimisation and ML workflows
subject: Articles
  topic: section
subject: Quantum Computing
  topic: keywords
subject: Quantum Algorithms
  topic: keywords
subject: Optimisation
  topic: keywords
subject: Quantum Approximate Optimization Algorithm (QAOA)
  topic: keywords
subject: Quantum Annealing
  topic: keywords
subject: Quantum Machine Learning (QML)
  topic: keywords
subject: Quantum Support Vector Machines
  topic: keywords
subject: Quantum Neural Networks
  topic: keywords
subject: Hybrid Quantum-Classical Systems
  topic: keywords
subject: Quantum Advantage
  topic: keywords
creator: Ravi Teja Avireneni
creator: Sri Harsha Koneru
creator: Naresh Kiran Kumar Reddy Yelkoti
creator: Sivaprasad Yerneni Khaga
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 149
ending page: 162
doi: 10.63282/3050-9262.IJAIDSML-V5I4P115

Dynamic Workload Placement across Multi-Clouds: AI-Driven Cost Optimization without Downtime
type: item
description: Dynamic workload placement across multiple cloud providers enables organizations to optimize cost, performance, and reliability. This paper proposes an AI-driven framework that continuously evaluates pricing, latency, and resource availability across providers such as AWS, Azure, and Google Cloud Platform (GCP) to determine the most cost-effective environment for each workload. The system dynamically migrates workloads between providers without downtime by using predictive analytics, live-migration containers, and federated orchestration policies. This conceptual study outlines the architecture, algorithms, and benefits of intelligent workload placement while highlighting challenges in interoperability, cost modeling, and compliance
subject: Articles
  topic: section
subject: Multi-Cloud
  topic: keywords
subject: Workload Placement
  topic: keywords
subject: AI Orchestration
  topic: keywords
subject: Cost Optimization
  topic: keywords
subject: Live Migration
  topic: keywords
subject: Cloud Federation
  topic: keywords
subject: AWS
  topic: keywords
subject: Azure
  topic: keywords
subject: GCP
  topic: keywords
subject: Hybrid Cloud
  topic: keywords
creator: Hema Vamsi Nikhil Katakam
rights: Copyright (c) 2025
date: 2025-11-19
publication date: 2025-11-19
starting page: 96
ending page: 100
doi: 10.63282/3050-9262.IJAIDSML-V6I4P114

Securing Broadband Subscriptions on SaaS with OAuth2.0 in REST API
type: item
description: The rapid evolution of cloud-native technologies such as Software as a Service (SaaS) has transformed the landscape for Broadband Service Providers (BSPs), enabling the delivery of subscriber services with high scalability and efficiency. Initially, the adoption of such platforms was driven with a cause for simplifying workflows and increase usability for BSP operational teams. Over time, however, both BSPs and their associated Operations Support Systems (OSS) and Business Support Systems (BSS) have become increasingly reliant on these cloud-based solutions. This has resulted in the accumulation and management of large quantities of sensitive data, including personally identifiable information (PII), as well as critical internet and voice service records for individual subscribers. Basic authentication method typically involved the exchange of simple username and password which were stored in databases. Basic auth. proved inadequate in securing sensitive information against evolving security threats. As regulatory requirements and industry standards have grown more stringent, BSPs have been compelled to transition to more robust authentication frameworks. OAuth 2.0 has emerged as the industry standard for secure data transport, offering enhanced protection through token-based authentication and granular access controls
subject: Articles
  topic: section
subject: OAuth 2.0
  topic: keywords
subject: REST API Security
  topic: keywords
subject: SaaS Security
  topic: keywords
subject: Broadband Service Providers (BSPs)
  topic: keywords
subject: Token-Based Authentication
  topic: keywords
subject: Access Control
  topic: keywords
subject: OSS/BSS Integration
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: PII Protection
  topic: keywords
subject: Secure Data Transport
  topic: keywords
creator: Amey Deshpande
rights: Copyright (c) 2025
date: 2025-11-16
publication date: 2025-11-16
starting page: 91
ending page: 95
doi: 10.63282/3050-9262.IJAIDSML-V6I4P113

Use of Federated Learning in Iiot (Industrial Internet of Things) Security
type: item
description: Federated learning (FL) is a distributed machine learning paradigm that enables devices or edge nodes to collaboratively train a global model without sharing raw data. This enhances privacy and reduces communication overhead. As Industrial Internet of Things (IloT) systems expand across manufacturing, energy, and logistics, security has become critical. Centralized learning approaches struggle with privacy risks, latency, and single points of failure. This article explores the application of FL to strengthen cyber-physical system (CPS) security in IIoT. We present a framework for threat detection, anomaly recognition, and intrusion prevention that utilizes lightweight models, secure aggregation, and differential privacy for resource-constrained industrial devices. Experiments on a simulated smart-factory testbed show that our approach achieves accuracy comparable to centralized methods while maintaining data locality and resilience. We also analyze trade-offs, challenges, and future directions for deploying FL in real-global lloT environments
subject: Articles
  topic: section
subject: Federated Learning (FL)
  topic: keywords
subject: Industrial Internet of Things (IIoT)
  topic: keywords
subject: Cyber-Physical Systems (CPS)
  topic: keywords
subject: Intrusion Detection Systems (IDS)
  topic: keywords
subject: Privacy-Preserving Machine Learning
  topic: keywords
creator: Bharathram Nagaiah
rights: Copyright (c) 2025
date: 2025-11-13
publication date: 2025-11-13
starting page: 87
ending page: 90
doi: 10.63282/3050-9262.IJAIDSML-V6I4P112

The Role of Artificial Intelligence in Predicting Credit Risk
type: item
description: Credit risk forecasting remains one of the key critical issues in financial risk management with the potential to impact lending rates, portfolio construction, capital allocation, and regulatory requirements. Conventional statistical techniques like logistic regression, discriminant analysis, and scorecard models have formed the backbone of credit assessment for many decades, but tend to be restricted by linear assumptions, limited learning ability, and difficulties in capturing non-linear behavioral characteristics (encapsulated in borrower data). There have been recent developments in the field of Artificial Intelligence (AI), in particular, machine learning (ML) and deep learning (DL), which have completely changed the paradigm for credit risk modelling. Via these methods, higher predictive performance can be achieved with the possibility of adapting to heterogeneous and high-dimensional data as well as integrating alternative and behavioral information, which classic modelling frameworks are unable to fully utilise. This paper provides an in-depth discussion on the potential of AI for credit risk estimation as well as its methodological upgrading, operational implementation regulatory frameworks that could support financial institutions applying AI-based scoring systems. Based on a review of the literature, ensemble learning techniques, and particularly gradient boosting techniques like XGBoost and LightGBM, have shown robust and discriminative performance against classical statistical models across studies, especially with noisy or missing data. Highly Nonlinear: Deep learning methods, with a surge in popularity, have shown inconsistent performances on structured credit data; they have been demonstrated to be effective only when including high-frequency non-linear features or complex behaviors, as well as unstructured information such as transaction sequences or text. The approach combines best practices from academia and industry for research to deployment, in…
subject: Articles
  topic: section
subject: Credit risk prediction
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Credit scoring
  topic: keywords
subject: Gradient boosting
  topic: keywords
subject: XGBoost
  topic: keywords
subject: LightGBM
  topic: keywords
subject: Model explainability
  topic: keywords
subject: SHAP values
  topic: keywords
subject: LIME
  topic: keywords
subject: Fairness in AI
  topic: keywords
subject: Financial risk modelling
  topic: keywords
subject: Probability of default
  topic: keywords
subject: Cost-sensitive learning; Model calibration
  topic: keywords
subject: Ensemble learning
  topic: keywords
subject: Financial regulation
  topic: keywords
subject: Algorithmic bias
  topic: keywords
subject: Risk assessment
  topic: keywords
creator: Surbhi Gupta
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 137
ending page: 148
doi: 10.63282/3050-9262.IJAIDSML-V5I4P114

Temporal Graph Neural Networks for Real-Time Fraud Detection in Cross-Border Transactions
type: item
description: Cross-border financial transactions are increasingly vulnerable to sophisticated and coordinated fraud attacks that evolve rapidly over time. Traditional fraud detection systems struggle to capture temporal dependencies and relational patterns inherent in real-time transaction networks. In this paper, we propose a novel approach using Temporal Graph Neural Networks (TGNNs) to detect fraudulent behavior in streaming, multi-national payment data. By modeling dynamic transaction graphs with time-aware message passing and temporal node embeddings, our framework captures both short-term anomalies and long-range dependencies. We design a low-latency inference pipeline capable of real-time deployment in financial networks. Experimental evaluation on synthetic and real-world cross-border transaction datasets demonstrates that our TGNN-based model significantly outperforms traditional machine learning and static GNN baselines, achieving up to 18% higher AUC and reducing false positives by 25%. The results highlight the potential of temporal graph learning to enhance security, compliance, and trust in global financial systems
subject: Articles
  topic: section
subject: Temporal Graph Neural Networks
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Cross-Border Payments
  topic: keywords
subject: Financial Transactions
  topic: keywords
subject: Streaming Graphs
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Time-Series Graph Learning
  topic: keywords
creator: Sai Vamsi Kiran Gummadi
rights: Copyright (c) 2025
date: 2025-11-10
publication date: 2025-11-10
starting page: 80
ending page: 86
doi: 10.63282/3050-9262.IJAIDSML-V6I4P111

The Effectiveness of Social Media in Customer Risk Awareness
type: item
description: Social media has taken centre stage as one of the most widespread avenues for information transfer and is fundamentally altering client attitude towards risk. Social media platforms are different than previous communication venues, from the print and broadcast press to institutional alerts, in that they are fast, interactive, algorithmically sensitive to each user’s profile, and with a capacity for distributed production of messages. These characteristics represent unprecedented opportunities for organizations, regulators, and public agencies to communicate risk information that is timely and actionable. At the same time, however, they add layers of volatility and uncertainty that make it harder for customers to understand real risks and allow misinformation, emotional hype, and unverified peer influence to warp that perception of risk. Given this context, the current study provides a comprehensive mixed-methods examination of how well social media increases customers’ awareness of risks in various contexts, including financial, public-health, and safety. The paper contends that effectiveness should be a multidimensional assessment-considering not just the increase in consumers' awareness of risk but also whether their awareness is accurate, translated into protective moves, and whether this communication setting attenuated distortion/exaggeration. Based on a 12-month content compendium and analyses of social media posts, a controlled experimental survey (N≈1200), and secondary enforcement actions and platform moderation policies, the study offers empirical evidence about what facilitates or hinders effective risk communication. The content analysis shows that official agencies generate very credible posts with a verifiable list of sources; however, these are dwarfed by the number of peer and influencer-owned pieces, which carry less authoritative footing and often feature sensational emotional framing. One fifth of the total sampled posts included deceptive markers…
subject: Articles
  topic: section
subject: Social Media
  topic: keywords
subject: Customer Risk Awareness
  topic: keywords
subject: Digital Risk Communication
  topic: keywords
subject: Misinformation
  topic: keywords
subject: Algorithmic Amplification
  topic: keywords
subject: Consumer Protection
  topic: keywords
subject: Risk Perception
  topic: keywords
subject: Credibility Assessment
  topic: keywords
subject: Behavioral Response
  topic: keywords
subject: Financial Risk
  topic: keywords
subject: Public Health Communication
  topic: keywords
subject: Crisis Messaging
  topic: keywords
subject: Platform Governance
  topic: keywords
subject: Online Trust
  topic: keywords
subject: Influencer Communication
  topic: keywords
creator: Surbhi Gupta
rights: Copyright (c) 2025
date: 2025-08-24
publication date: 2025-08-24
starting page: 107
ending page: 114
doi: 10.63282/3050-9262.IJAIDSML-V6I3P116

AI-Enhanced Event Tracking: A Collaborative Full-Stack Model for Tag Intelligence and Real-Time Data Validation
type: item
description: The current digital analytics systems strongly depend on precise tracking of events in order to measure user interactions, drive personalization pipelines, and make business decisions that are revenue critical. Nevertheless, current tagging systems still have the issue of fragmented instrumentation, the inconsistency of schema compliance, such as data drift and this interestingly missed event without any exception-in-curring serious costs in data quality and the performance of end model. To overcome them, this paper suggests an AI-Enhanced Event Tracking Framework which operates on a collaborative full-stack design to bind the front-end tag instrumentation, middleware validation logic and backend real-time anomaly detection into one intelligence-driven system. The proposed architecture takes advantage of three layers of automation: (i) an intelligent front-end instrumentation engine based on dynamically validating the DOM context, identification of broken or hand-fired tags and enrichment events with contextual metadata; (ii) policy-based middle layer based on the enforcement of event contracts, business rules, and schema constraints with the use of deterministic rules augmented by ML-based decision scoring; and (iii) a back-end AI validation engine based on prediction of event correctness by means of supervised classifier, sequence level anomaly detection by unsupervised drift model and the empirical analysis of production scale data sets substantiates that the system leads to the enhancement of data reliability end to end considerably. Findings have indicated 41-63 percent decrease in the missing or malformed events, 28 percent decrease in the duration of validation as well as 35-52 percent enhancement in the accuracy of detection of anomalies relative to the base rule-based validators. Moreover, the architecture allows high-throughput streaming operations to be maintained with real-time inference of more than 50k events per second at minimal overhead. Together, t…
subject: Articles
  topic: section
subject: AI-Enhanced Event Tracking
  topic: keywords
subject: AI-Driven Data Analytics
  topic: keywords
subject: Intelligent Tag Governance
  topic: keywords
subject: Reactive Frontend Analytics
  topic: keywords
subject: Trustworthy Digital Ecosystems
  topic: keywords
subject: Full-Stack Analytics Architecture
  topic: keywords
subject: Multi-Tenant Tracking Infrastructure
  topic: keywords
creator: Ravindra Putchakayala
creator: Rajesh Cherukuri
rights: Copyright (c) 2024
date: 2024-06-30
publication date: 2024-06-30
starting page: 130
ending page: 143
doi: 10.63282/3050-9262.IJAIDSML-V5I2P115

Governance-of-Things (GoT): A Next-Generation Framework for Ethical, Intelligent, and Autonomous Web Data Acquisition
type: item
description: The unstoppably increasing number of the Internet of Things (IoT), autonomous agents, and massive distributed web ecosystems have made data acquisition a complicated, risk-prone, and a very sensitive process. Regulation Web data collection is a fixed pipeline that is strictly regulated by established rules and legal limits, and reactive policy audits to operate in traditional forms of governance. Nevertheless, the contemporary digital ecosystem requires a decentralized system of governance that could identify unpredictable streams of data, the shifting web framework, loosely distributed computing individuals, and shifting conditions of regulation. This paper will present Governance-of-Things (GoT), an emerging conceptual and architectural design that will address these issues and show how to smoothly integrate ethical intelligence, regulatory and laws compliance, semantic awareness, and integrity assurance within autonomous systems of web data acquisition. GoT suggests a view where governance follows a first-class computation i.e. embedded, adaptive, intelligent and context-aware. In contrast to traditional approaches of governing IoT, GoT regards any acquisition agent as ethics-regulated, compliance-aware, and self-regulating. Agents do not simply pull information, they negotiate access rights, authenticate provenance, reason about risk, and implement multi-jurisdictional policies all by themselves. The framework combines dynamic enforcement of policies, federated governance, semantic classification pipelines, AI-enhanced agent frameworks built on Java and distributed analytics to create an ecosystem, producing an automated acquisition that is compatible with responsible, transparent, and audit-friendly behaviours. Fairness, legality, transparency, explainability and accountability are the principles of ethical autonomy which are expounded in the paper. GoT has the aspect of federated ethical rule orchestration where the governance layers among organizations in var…
subject: Articles
  topic: section
subject: Governance-Of-Things
  topic: keywords
subject: Autonomous Acquisition
  topic: keywords
subject: Compliance-Aware Agents
  topic: keywords
subject: Dynamic Policy Enforcement
  topic: keywords
subject: Federated Governance
  topic: keywords
subject: Semantic Classification
  topic: keywords
subject: Integrity-Preserving Automation
  topic: keywords
subject: Adaptive Monitoring
  topic: keywords
subject: AI-Enhanced Java Frameworks
  topic: keywords
subject: Distributed Analytics
  topic: keywords
creator: Rohit Yallavula
creator: Ravindra Putchakayala
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 111
ending page: 120
doi: 10.63282/3050-9262.IJAIDSML-V4I4P113

AI-Enabled Policy-Driven Web Governance: A Full-Stack Java Framework for Privacy-Preserving Digital Ecosystems
type: item
description: The digital ecosystems have experienced a paradigm shift as there is a growing level of integration of Artificial Intelligence (AI), distributed computing apparatus, and robotic policy enforcement strategies. Governance structures are faced with the difficult task of negotiating the vagaries of privacy laws and decentralized data processing and the use of algorithmic decision-making with the migration of data-intensive applications to web-based environments. The increasing regulatory environment, such as GDPR, CCPA, and industry-specific data protection requirements, have significant forces on the requirement to have strong policy-driven governance infrastructures that entrench privacy and security at every layer of the web application stack. Although cloud platforms and microservice architectures have been developed, modern governance solutions have weaknesses in terms of scalability, being context-aware and dynamically adapting to changes in policy constraints. The research paper presents the AI-Enabled Policy-Driven Web Governance Framework that has been developed on the Full-Stack Java ecosystem which involves spring boot, Jakarta EE, containerized deployment platforms and intelligent agents which are rule-based. The framework incorporates machine learning-related policy interpretation, semantic arguments engines, as well as automated monitoring applications that regulate user interactions, data activities, service coordination, and cross-layer correspondence. AI agents will adapt legal and organizational privacy requirements into dynamic policies that are explicitly and dynamically implemented in real-time at the front-end, API, middleware, and database tiers. These challenges in digital governance that are solved are minimization of data, contextual privacy, verification of compliance, detection of anomalies, and fine-grained access control. The given architecture proposes a Multi-Layer Governance Orchestration Model (MGOM) that divides the governance issues i…
subject: Articles
  topic: section
subject: AI-Enabled Web Governance
  topic: keywords
subject: Policy-Driven Architecture
  topic: keywords
subject: Privacy-Preserving Digital Ecosystems
  topic: keywords
subject: Adaptive Governance Intelligence
  topic: keywords
subject: Data Integrity Management
  topic: keywords
subject: Governance Automation Models
  topic: keywords
subject: Enterprise-Scale Security Engineering
  topic: keywords
subject: AI-Enabled Compliance Automation
  topic: keywords
creator: Ravindra Putchakayala
creator: Rajesh Cherukuri
rights: Copyright (c) 2022
date: 2022-03-30
publication date: 2022-03-30
starting page: 114
ending page: 123
doi: 10.63282/3050-9262.IJAIDSML-V3I1P112

What’s the True ROI of Copay Programs? A Transparent Framework for Evaluation
type: item
description: The copay assistance benefit programs lower out of pocket (OOP) patient expenses, and may enhance therapy initiation and compliance, but their actual ROI is commonly lost in confounding policy processes and accounting methods. The present paper gives a clear audit-compliant structure of ROI as a risk-adjusted incremental contribution margin assigned to copay exposure excluding gross-to-net factors (rebates, chargebacks, fees), operating costs, and compliance risk. The framework breaks down impact into four causal levers initiation, adherence/persistence, channel steering and price elasticity and relates them to what can be observed (incremental new starts, days on therapy, refill cadence, payer mix). It will be identified using quasi-experimental designs (difference-in-differences, regression discontinuity, instrumental variables, and synthetic controls) and survival analysis to ascribe adherence benefits to lifetime value. An explicit guideline on policies such as accumulator/maximizer program, step edits and specialty carve-outs is to keep benefits overstated. Governance guardrails are pre-registered analysis plans, data lineage, and sensitive panels are included, making results comparable across brands and time. Applied scenarios reveal that copay programs are value-generating under high elasticity and abandonment risk and high accumulator exposure and low cannibalization and high policy frictions, but value-destroying under dominance of cannibalization and high policy frictions, respectively. What it delivers is an effective toolkit that standardizes ROI equations, decision dashboards, and scenario testing that will allow financial stewardship to meet patient access and allow credible reporting to manufacturers, payers, providers and researchers
subject: Articles
  topic: section
subject: Copay assistance
  topic: keywords
subject: ROI
  topic: keywords
subject: gross-to-net (GTN)
  topic: keywords
subject: incrementality
  topic: keywords
subject: difference-in-differences
  topic: keywords
subject: regression discontinuity
  topic: keywords
subject: accumulator/maximize
  topic: keywords
creator: Tushar Kandwal
rights: Copyright (c) 2025
date: 2025-11-07
publication date: 2025-11-07
starting page: 71
ending page: 79
doi: 10.63282/3050-9262.IJAIDSML-V6I4P110

Generative AI for Network Attack Simulation
type: item
description: Generative Artificial Intelligence (AI) holds remarkable possibilities in a host of applications, such as cybersecurity and various other fields. This paper presents and examines the application of Generative AI for the Simulation of Network attacks. It highlights its key characteristics, areas of cyber security and data science, challenges, and opportunities in addressing the problem of cybercrimes. The research presented in this paper explores the use of Generative AI techniques, such as GANs, to simulate complex cyberattacks with rigor and reliability, thereby spurring the authentication of sophisticated phishing algorithms and bolstering the fortification of sophisticated cybersecurity systems. This study aims to evaluate the feasibility of using AI to conduct simulated network attacks compared to traditional methods. It is important to note that this research was conducted authentically and was done entirely through the care, process, and support of the designated University. In addition, the organizations can foresee the defects within their network and respond much quickly by simulating realistic attack scenarios through patented technologies (such as Generative AI). Subsequently, it proposes that research in this area is also essential to continually assist in innovating responsibly and to address potential risks associated with increasing autonomy of AI systems, e.g., risks of AI systems having a security exploit with button content set as skip and the attribute set as. Additionally, adopting machine learning approaches for Network Attack Simulation would offer more reliable insights into predicting adversary movements, thereby enabling the design of security measures that can potentially prevent critical states in linear time. At the same time, a future emerged where we barely understand the generative adversarial network. In that future, we will need to identify them by leveraging our knowledge of Generative AI technologies and capabilities
subject: Articles
  topic: section
subject: Generative Artificial Intelligence (Generative AI)
  topic: keywords
subject: Network Attack Simulation
  topic: keywords
subject: Cybersecurity Simulation
  topic: keywords
subject: AI-driven Cyber Attack Modeling
  topic: keywords
subject: Adversarial AI
  topic: keywords
creator: Harshith Kumar Pedarla
rights: Copyright (c) 2025
date: 2025-11-04
publication date: 2025-11-04
starting page: 65
ending page: 70
doi: 10.63282/3050-9262.IJAIDSML-V6I4P109

Federated Learning Infrastructure for Privacy-Preserving Personalized Shopping in E-Commerce
type: item
description: The modern e-commerce landscape is defined by a fundamental tension between the strategic imperative for real-time, personalized user experiences and the dual constraints of stringent privacy regulations and high-latency computation. While personalized recommendations are critical for navigating information overload and driving engagement, traditional centralized architectures pose significant data privacy risks. Federated Learning (FL) has emerged as a foundational, privacy-preserving paradigm by training models on decentralized user data. However, standard FL implementations suffer from a severe communication bottleneck, introducing latency that is prohibitive for time-sensitive applications like product recommendations at checkout. This paper addresses this performance gap by introducing a new class of tooling exemplified by the Low-Latency Federated Learning (LoLaFL) framework. It details the architectural shift from conventional backpropagation-based FL to a novel forward-only, white-box framework. This approach dramatically reduces communication overhead and computational complexity, enabling the low-latency inference required to deliver secure, private, and instantaneous personalized recommendations at the most critical point of the customer journey
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Low-Latency AI
  topic: keywords
subject: Personalized Recommendations
  topic: keywords
subject: E-commerce
  topic: keywords
subject: Privacy-Preserving Machine Learning
  topic: keywords
subject: Real-Time Inference
  topic: keywords
subject: LoLaFL
  topic: keywords
creator: Udit Agarwal
creator: Aditya Gupta
rights: Copyright (c) 2025
date: 2025-11-01
publication date: 2025-11-01
starting page: 59
ending page: 64
doi: 10.63282/3050-9262.IJAIDSML-V6I4P108

Exponential Pine Cone Optimization Enabled Quantum-Inspired Convolutional Neural Networks for Secure Network Slicing in 5G Network
type: item
description: Network Slicing is significant in facilitating a multiple of Fifth-generation (5G) applications, use cases, and services. It provides end-to-end isolation among the slices for customizing each slice in terms of service demands. The increasing demands of 5G networks and advanced features to meet complex business needs have rendered conventional approaches insufficient. Therefore, an efficient approach is required to mitigate the issues in the slicing process. As a result, Exponential Pine Cone Optimization enabled Quantum-Inspired Convolutional Neural Network (EPCO_QuCNet) is introduced for secure network slicing in 5G network. The system model of 5G network is carried out. When network slicing receives requests from a User Equipment (UE), various network parameters, like speed, packet loss rate, jitter, and packet delay budget—are gathered from multiple devices. Hence, a secure slicing of network is classified by Quantum-Inspired Convolutional Neural Networks (QuCNet), which is trained by Exponential Pine Cone Optimization (EPCO) algorithm. Furthermore, the services provided in 5G is accessed by the internet service provider using Virtual Network Function (VNF). EPCO_QuCNet has achieved better outcomes for the evaluation metrics, like acceptance rate, execution time and resource efficiency of 0.927, 0.167 sec and 0.911
subject: Articles
  topic: section
subject: Network slicing
  topic: keywords
subject: 5G network
  topic: keywords
subject: Quantum-Inspired Convolutional Neural Networks
  topic: keywords
subject: Exponential Weight Moving Average
  topic: keywords
subject: Pine Cone Optimization Algorithm
  topic: keywords
creator: Saikiran Chevuru
rights: Copyright (c) 2025
date: 2025-02-18
publication date: 2025-02-18
starting page: 209
ending page: 217
doi: 10.63282/3050-9262.IJAIDSML-V6I1P123

Architecting, Grounding and Governing – Telecom Supply Chain GenAI Agents with SAP AI Foundation
type: item
description: Modern supply chains demand unprecedented agility, resilience, and intelligence to navigate the volatility of rapid technology cycles, complex network rollouts, and critical infrastructure dependencies. Generative AI (GenAI) agents offer transformative potential, automating complex decision-making and optimizing processes. However, realizing this potential requires robust frameworks for their architecture, grounding in reliable data, and responsible governance. This white paper details a comprehensive approach to architecting, grounding, and governing Telecom Supply chain GenAI agents leveraging SAP AI Foundation on the SAP Business Technology Platform (BTP). It explores methodologies for building context-aware, reliable, and ethically aligned agents using components like SAP AI Core, Generative AI Hub, SAP HANA Cloud Vector Engine, and SAP Knowledge Graph. The paper outlines the benefits, including enhanced efficiency, enhanced network resilience, improved field service efficiency, optimized inventory, improved decision-making, and increased resilience, alongside implementation considerations and use cases, targeting supply chain professionals, IT/AI architects, and business leaders seeking to harness GenAI responsibly within the SAP ecosystem and architecting Gen AI agents
subject: Articles
  topic: section
subject: Telecom Supply Chain Management (SCM)
  topic: keywords
subject: Generative AI (GenAI)
  topic: keywords
subject: AI Agents
  topic: keywords
subject: SAP AI Foundation
  topic: keywords
subject: SAP Business Technology Platform (BTP)
  topic: keywords
subject: Architecting
  topic: keywords
subject: Grounding
  topic: keywords
subject: Governing
  topic: keywords
subject: SAP AI Core
  topic: keywords
subject: Generative AI Hub
  topic: keywords
subject: Joule Studio
  topic: keywords
subject: SAP HANA Cloud Vector Engine
  topic: keywords
subject: SAP Knowledge Graph
  topic: keywords
subject: Retrieval Augmented Generation (RAG)
  topic: keywords
subject: AI Ethics
  topic: keywords
subject: Responsible AI
  topic: keywords
subject: Supply Chain Optimization
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Network Rollout
  topic: keywords
subject: Spare Parts Logistics
  topic: keywords
subject: 5G
  topic: keywords
creator: Sivasubramanian Kalaiselvan
rights: Copyright (c) 2025
date: 2025-10-28
publication date: 2025-10-28
starting page: 49
ending page: 58
doi: 10.63282/3050-9262.IJAIDSML-V6I4P107

Privacy-Preserving Federated Learning Frameworks for Telematics Data in Auto Insurance Analytics
type: item
description: In particular, the use of telematics data in auto insurance has dramatically changed risk underwriting and pricing but raises serious privacy issues for drivers sensitive information. In this work, we investigate privacy-preserving federated learning methods for fostering collaborative machine learning between multiple insurance providers that does not require centralization of telematics data. Our work explores use of differential privacy, homomorphic encryption, and secure aggregation to safeguard driver behavior patterns, location data and vehicular telemetry while preserving prediction accuracy. We conjecture that hybrid privacy approaches for federated learning architectures are capable of providing the same level of model performance as centralized ones guaranteeing a strong security and privacy level in data. By analyzing real-world deployment scenarios, we find that when using privacy-preserving frameworks the privacy leakage is reduced by 73-85%, while model accuracy remains above 91%. The work shows that homomorphic encryption based aggregation can provide better privacy with 8-12% computing overhead. These findings show that federated learning frameworks can be potential solutions to insurance telematics analytics, which allows for risk modeling in a collaborative way under data protection regulation and competitive intelligence through privacy-preserving collaboration mechanism
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Telematics Data
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Auto Insurance Analytics
  topic: keywords
subject: Differential Privacy
  topic: keywords
creator: Abhijit Ubale
rights: Copyright (c) 2025
date: 2025-10-25
publication date: 2025-10-25
starting page: 41
ending page: 48
doi: 10.63282/3050-9262.IJAIDSML-V6I4P106

Conversational AI in Salesforce: A Study of Einstein Bots and Natural Language Understanding
type: item
description: Chatbot AI is turning into an essential part of the contemporary customer management beliefs (CRM). In order to automate customer support, lead generation, and the process of workflow management, Salesforce, a market leader in CRM, has introduced AI-driven virtual dimininiske povrh2718 noticeable bots, so-called Einstein Bots. The present paper assesses the success of the Salesforce Einstein Bots with reference to Natural Language Understanding (NLU), end-user satisfaction and real-life issues of implementation. We discuss the issues of conversational models in Salesforce regarding multilingual input, intents identification, extraction of entities and fallback cases. The paper is based on a review of literature, architectural survey and simulation of enterprise. In our findings, it has been indicated that Einstein Bots can work in structured settings but they have a weakness on contextual awareness and interpretation of sentiments. The paper provides suggestions on how to improve NLU pipelines and describes the best practice on optimising user experience. Our strategic recommendations to strike a balance between the effectiveness of automation and user activity in chatbot implementations in Salesforce are the conclusion
subject: Articles
  topic: section
subject: Conversational AI
  topic: keywords
subject: Salesforce Einstein Bots
  topic: keywords
subject: Natural Language Understanding (NLU)
  topic: keywords
subject: Chatbots in CRM
  topic: keywords
subject: Intelligent Virtual Assistants
  topic: keywords
subject: AI-driven Customer Service
  topic: keywords
subject: Dialogue Management
  topic: keywords
subject: Natural Language Processing (NLP)
  topic: keywords
subject: Machine Learning in Salesforce
  topic: keywords
creator: Shalini Polamarasetti
rights: Copyright (c) 2023
date: 2023-10-30
publication date: 2023-10-30
starting page: 98
ending page: 102
doi: 10.63282/3050-9262.IJAIDSML-V4I3P111

Hybrid AI-Based Threat Prediction and Mitigation Framework for Secure Cloud Storage: A Rigorous Critical Review of Methodological Challenges and Future Research Directions
type: item
description: In the fast-changing world of cloud computing, maintaining the confidentiality, integrity, and availability of data is still a major challenge. The research suggests a framework of Hybrid Artificial Intelligence (AI)-Based Threat Prediction and Mitigation which is intended to improve the security of cloud storage by using integrated machine learning (ML) and deep learning (DL) techniques. The study uses a statistically validated dataset (N = 50) that contains specifications like AI model accuracy, false positive rate, p-value, cloud storage security score, mitigation efficiency, and detection latency. Through descriptive and inferential analyses, it is revealed that the model obtains 91.2% overall accuracy, 2.44% false positive rate while at the same time, the average mitigation efficiency is 84.5% plus the latency of detection is 104 ms. The p-value = 0.00259 < 0.005 indicates that the improvement in the model’s performance is statistically significant. The findings imply that hybrid AI techniques can effectively reduce the number of false alerts and improve the speed of real-time response in the situation of distributed cloud infrastructures. The proposed framework is shown to be wide-ranging, flexible, and dependable in terms of prediction, thereby playing a part in making the cloud ecosystems secure and self-sufficient
subject: Articles
  topic: section
subject: Hybrid Artificial Intelligence
  topic: keywords
subject: Cloud Storage Security
  topic: keywords
subject: Threat Prediction
  topic: keywords
subject: Mitigation Efficiency
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Statistical Validation
  topic: keywords
subject: Cyber Threat Intelligence
  topic: keywords
subject: Performance Optimization
  topic: keywords
creator: Anam Haider Khan
rights: Copyright (c) 2024
date: 2024-04-30
publication date: 2024-04-30
starting page: 121
ending page: 129
doi: 10.63282/3050-9262.IJAIDSML-V5I2P114

AI-Enhanced ETL Framework for Improving Data Quality in Clinical Decision Support Systems
type: item
description: Clinical Decision Support System (CDSS) are inalienable parts of modern healthcare in that they offer an informational support to clinicians regarding diagnosis, treatment planning, and management of patients. Their credibility however depends on the quality of the information obtained on the Electronic Health Records (EHRs) and other heterogeneous sources. Lacking, disjunctive, and semantically varied data remain as major hindrances to fruitful decision-making. This paper suggests an AI-powered Extract, Transform, Load (ETL) system that relies on machine learning, natural language processing, and ontology-based reasoning systems to improve healthcare data quality automatically. The framework uses anomaly detection using autoencoders, entity detection using BioBERT, and semantic harmonisation using HL7-FHIr and SNOMED-CT ontologies. A feedback loop that is reinforcement-learning further optimises changes with time. The experimental analysis of publicly available MIMIC-III and PhysioNet data indicates a 26% high data completeness, a 17% higher rate in consistency, and a 10% higher rate of CDSS diagnostic stability compared to the traditional ETL tasks. These results show that AI-based ETL technology can significantly improve data quality, interoperability, as well as generating clinical insights, thus leading to a platform that enables more credible and scalable CDSS designs
subject: Articles
  topic: section
subject: Clinical Decision Support Systems
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: ETL
  topic: keywords
subject: HL7 -FHIR
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Health Informatics
  topic: keywords
subject: Ontology
  topic: keywords
creator: Chitiz Tayal
rights: Copyright (c) 2024
date: 2024-06-30
publication date: 2024-06-30
starting page: 116
ending page: 120
doi: 10.63282/3050-9262.IJAIDSML-V5I2P113

Big Data Pipeline Optimisation for Electronic Health Records (EHR)
type: item
description: The vast utilisation of Electronic Health Records (EHRs) has led to a revolution in healthcare organisations worldwide. These big data are the major factors for the predictive analysis process, which contributes to efficient patient care and mitigation of potential risks at an early stage. The machine learning algorithms benefit the healthcare system with in-depth analysis of the large datasets by avoiding complexity that leads to the development of automated models that enforce swift and efficient treatment of patients. Despite being advantageous for healthcare individuals, they lag in ensuring the privacy and anonymity of patient data. Future research will be effective in addressing these ethical shortcomings and lead to clinical trustworthiness. Machine learning approaches like SVR and K-means clustering have been used to deliver precise insights from the patient records, and this determines the training machine learning model, enforcing efficient patient service. In addition to this, the evaluation of the analysis section orchestrated the optimisation of the big data pipeline in electronic health records is important to enhance the operational excellence of the healthcare system and standardise the entities to provide better treatments to the patients. In the context of future work, the presentation of new approaches of artificial intelligence and deep learning could be effective in optimising the functionalities and features of the electronic health record further with immaculate accuracy
subject: Articles
  topic: section
subject: Electronic Health Records
  topic: keywords
subject: Big Data
  topic: keywords
subject: Big Data Pipelines
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Support Vector Regression
  topic: keywords
subject: K-Mean Clustering Algorithm
  topic: keywords
creator: Chitiz Tayal
rights: Copyright (c) 2024
date: 2024-10-30
publication date: 2024-10-30
starting page: 121
ending page: 127
doi: 10.63282/3050-9262.IJAIDSML-V5I3P113

Data Quality Assessment and Cleaning Framework for Healthcare Databases Using Python
type: item
description: The arrival of data-driven healthcare systems requires high-quality data sets to enable clinical research and decision-making. However, healthcare databases always contain various data quality issues, including inconsistency, duplication, and logical error, which prevent the analysts to conduct an analysis that has validation. This paper develops a DQACFHD that integrates advanced theories of data quality management with the feasible procedures for implementing the theory. We retrieved a public health care data set from Kaggle and utilized it to implement our DQACFHD tool. The methodologies conducted data profiling, rule-based validation technique, duplicate identification, outlier removal, and logical inconsistency checks using the four quality dimensions. These are dubbed completeness, uniqueness, validity, and accuracy. We utilized Pythons libraries, such as Pandas, NumPy, Matplotlib, to automate the data cleaning process and machine learning model and visualize the data quality post-processing. Our DQACFHD tool removed all the duplicates and inconsistencies in the data, as evidenced by the 100% score on the quality dimensions by applying different strategies in the automated cleaning process. The post-processing analysis further demonstrated a realistic data distribution across demographic and financial dimensions. Therefore, the results show how automation based on Python has made it realistic to attain high data quality, efficiency, and reproducibility for health purposes given the application use. Thus, the application may ensure better data in different health data applications. The tool can also facilitate future work on data governance and analytical frameworks
subject: Articles
  topic: section
subject: Healthcare Data Quality
  topic: keywords
subject: Data Cleaning Framework
  topic: keywords
subject: Data Quality Assessment
  topic: keywords
subject: Python Programming
  topic: keywords
subject: Data Preprocessing
  topic: keywords
subject: Data Consistency
  topic: keywords
subject: Data Completeness
  topic: keywords
subject: Data Validity
  topic: keywords
subject: Electronic Health Records (EHR)
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
subject: Duplicate Detection
  topic: keywords
subject: Data Profiling
  topic: keywords
subject: Rule-Based Validation
  topic: keywords
subject: Automated Data Cleaning
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Data Quality Dimensions
  topic: keywords
subject: Pandas
  topic: keywords
subject: NumPy
  topic: keywords
subject: Seaborn
  topic: keywords
subject: Real-World Healthcare Data
  topic: keywords
creator: Chitiz Tayal
rights: Copyright (c) 2022
date: 2022-12-30
publication date: 2022-12-30
starting page: 107
ending page: 112
doi: 10.63282/3050-9262.IJAIDSML-V3I4P112

Encryption Strategies for Secure Big Data Storage: A Study of AWS S3 and Redshift Clusters
type: item
description: As the big data continues to grow exponentially, safeguarding the data and having the tighten access control mechanisms has become a problem that enterprises and researchers are in need of. Scalable and cost effective storage solutions are meted out via cloud based storage solutions such as Amazon Web Services (AWS) Simple Storage Service (S3) and Redshift. However, these services require numerous encryption strategies in place to make the data safe, confidential, secure, and compliant with the industry standards. In this paper we discuss the encryption techniques to be used in securing big data inside the AWS S3 and Redshift clusters. A comparative performance, security effectiveness and computational overhead analysis of server side encryption (SSE), client side encryption (CSE) and column level encryption are made. In addition to this, the study introduces the mathematical models to evaluate encryption performance, along with an optimum encryption framework to provide a balance between security and performance for big data workloads. These findings provide insight into the encryption mechanisms of cloud storage based encryption and give some guidelines how efficient and secure encryption policies can be achieved
subject: Articles
  topic: section
subject: Big Data Security
  topic: keywords
subject: AWS S3
  topic: keywords
subject: Redshift Clusters
  topic: keywords
subject: Encryption Strategies
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Server-Side Encryption
  topic: keywords
subject: Client-Side Encryption
  topic: keywords
subject: Performance Analysis
  topic: keywords
creator: Naga Surya Teja Thallam
rights: Copyright (c) 2021
date: 2021-12-03
publication date: 2021-12-03
starting page: 70
ending page: 80
doi: 10.63282/3050-9262.IJAIDSML-V2I4P108

When AI Acts: Opportunities and Risks of Agentic Systems
type: item
description: An agentic artificial intelligence (AI) marks a revolutionary new class of system that can perform, on its own, actions that are directed towards achieving some goals. In contrast to traditional reactive AI models, which simply respond to input, agentic systems comprehend their surroundings, reason strategically and can even work independently in a changing environment. Their birth is in line with the development of large language models, reinforcement learning and autonomous orchestration frameworks, which give machines the ability to plan and carry out tasks without the need for constant human intervention. This paper is about the issues connected with the agentic AI that make it so popular at this moment in time, the benefits brought by it and the hazards it causes. The possible uses extend from robotics and cybersecurity to finance and scientific discovery, where the capability of making decisions independently can open up the vast efficient use of time. However, the existence of these systems means the possibility of substantial risks, such as among others, misalignment, emergent behaviors, accountability gaps, and ethical uncertainties. The current article makes a detailed conceptualization framework for the comprehension of agentic AI: it specifies the features of the system, shows its track, considers the implications, and suggests the strategies for management, as well as, by providing the example of AutoGPT, tries to convey both the promise and the danger in the autonomous action of AI
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Autonomous Systems
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Autogpt
  topic: keywords
subject: Ethical AI
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: Adaptive Autonomy
  topic: keywords
subject: Artificial Intelligence Safety
  topic: keywords
creator: Adityamallikarjunkumar Parakala
creator: Preston Padgett
rights: Copyright (c) 2025
date: 2025-10-22
publication date: 2025-10-22
starting page: 29
ending page: 40
doi: 10.63282/3050-9262.IJAIDSML-V6I4P105

Emergence of AI Trust Layers & Governance
type: item
description: The rapid ascent of artificial intelligence has opened up a plethora of possibilities for various sectors; however, it has equally generated some very severe problems with regard to trust, accountability, and the responsible use of the technology. Consequently, as AI systems progressively become the mediators of the decisions in the fields of medical care, finance, governance, and also in our daily lives, issues with fairness, transparency, bias, and taking advantage of the technology have surfaced. There is the concept of “trust layers”, which has been propounded to reconcile this widening gulf that is between dependability and innovation – these are specialised mechanisms, frameworks, and safeguards that double up as checks and balances, assuring that AI is not only efficient but safe, ethical, and in line with human values. The technical guardrails that track the models in real time, the organisational processes that act as a governor on how AI is deployed, and the societal oversight that weighs innovation against collective well-being are some of the levels these layers function in. Decision-making is the core of these trust layers where governance is situated and which acts as the backbone of the responsible conduct of AI by setting standards, forming accountability structures, and instituting transparent practices that make the trust grow with the use. This article outlines how governance, combined with trust-enabling layers, is a game changer for AI making it a technology of certainty rather than one of doubt. We develop a coherent framework demonstrating the implementability of trust layers all through the AI lifecycle, practically facilitated through governance examples. So as to better understand these concepts, we add a real-life situation that exemplifies how these ideas were implemented in an actual AI case, thereby deriving learnings from both triumphs and predicaments. Collectively, these revelations confirm that trust is not a stumbling block to but …
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Trust Layers
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subject: AI Governance
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subject: Responsible AI
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subject: AI Ethics
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subject: Regulatory Compliance
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subject: Transparency
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subject: Accountability
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subject: Fairness
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subject: AI Policy
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subject: Case Studies
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creator: Adityamallikarjunkumar Parakala
rights: Copyright (c) 2025
date: 2025-05-17
publication date: 2025-05-17
starting page: 144
ending page: 152
doi: 10.63282/3050-9262.IJAIDSML-V6I2P116